Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter
Abstract of Pulmonary Fissure Detection in CT Images Using a Derivative Stick
Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter.Pulmonary fissures are important landmarks for recognition of lung anatomy. To circumvent this problem, we propose a derivative of stick (DoS) filter for fissure enhancement and a post-processing pipeline for subsequent segmentation. Then, to accommodate pathological abnormality and orientational deviation, a max-min cascading
and multiple plane integration scheme is adopted to form a shape-tuned likelihood for 3D surface patches discrimination.
The good performance of our proposed method was also verified by visual inspection
and demonstration on abnormal and pathological cases, where typical deformations were robustly detected together with normal fissures.
Discussion and Conclusion
We proposed a derivative of stick filter and a post-processing segmentation pipeline for pulmonary fissures detection in thoracic CT images. The principle of our filter originated from an observation that the 3D surface shape of fissures can be simplified to an equivalent co-linear constraint across multiple section planes.
In the post-processing stage, the 3D continuity of the fissure surface is further exploited to separate adhering clutters by introducing a branch-point removal algorithm.
